In the domain of unmanned vehicles, autonomous robotic swarms promise to deliver increased efficiency and collective autonomy. How these swarms will operate in the future, and what communication requirements and operational boundaries will arise are yet to be sufficiently defined. A workshop was conducted with 11 professional unmanned-vehicle operators and designers with the objective of identifying use-cases for developing and testing robotic swarms. Three scenarios were defined by experts and were then compiled to produce a single use case outlining the scenario, objectives, agents, communication requirements and stages of operation when collaborating with highly autonomous swarms. Our compiled use case is intended for researchers, designers, and manufacturers alike to test and tailor their design pipeline to accommodate for some of the key issues in human-swarm ininteraction. Examples of application include informing simulation development, forming the basis of further design workshops, and identifying trust issues that may arise between human operators and the swarm.

When writing, a person may need to anticipate questions from their audience, but different social groups may ask very different types of questions. If someone is writing about a problem they want to resolve, what kind of follow-up question will a domain expert ask, and could the writer better address the expert's information needs by rewriting their original post? In this paper, we explore the task of socially-aware question generation. We collect a data set of questions and posts from social media, including background information about the question-askers' social groups. We find that different social groups, such as experts and novices, consistently ask different types of questions. We train several text-generation models that incorporate social information, and we find that a discrete social-representation model outperforms the text-only model when different social groups ask highly different questions from one another. Our work provides a framework for developing text generation models that can help writers anticipate the information expectations of highly different social groups.

Contemporary machine learning (ML) techniques surpass traditional statistical methods in terms of their higher predictive power and their capability of processing a larger number of attributes. However, these novel ML algorithms generate models that have a complex structure which makes it difficult for their outputs to be interpreted with high precision. Another important issue is that a highly accurate predictive model might lack fairness by generating outputs that may result in discriminatory outcomes for protected subgroups. Thus, it is imperative to design predictive systems that are not only accurate but also achieve the desired fairness level. When used in certain contexts, predictive models, and strategies that rely on such models, are subject to laws and regulations that ensure fairness.

A case currently before the U.S. Court of Appeals must decide if artificial intelligences should have patent rights on processes they were used to design. Throwing a wrench into the works, OpenAI has announced that artists using its new DALL-E beta software can sell the work: "Starting today, users get full usage rights to commercialize the images they create with DALL·E, including the right to reprint, sell, and merchandise. This includes images they generated during the research preview." First, here's what DALL-E can do: OpenAI's press release for DALL-E 2 markets the advanced tech as a "powerful creative tool" that will speed up and inspire the creative process. But as some have already started to point out, the ability to commercialize DALL-E 2 will likely have a pretty major impact on creative industries -- and some of the resulting ramifications may not be good.

This week, OpenAI granted users of its image-generating AI system, DALL-E 2, the right to use their generations for commercial projects, like illustrations for children's books and art for newsletters. DALL-E 2 "trained" on approximately 650 million image-text pairs scraped from the internet, learning from that dataset the relationships between images and the words used to describe them. But while OpenAI filtered out images for specific content (e.g. As the AI community creates open source implementations of DALL-E 2 and its predecessor, DALL-E, both free and paid services are launching atop models trained on less-carefully filtered datasets. When contacted for comment, the Pixelz.ai

Esther Shein is a freelance technology and business writer based in the Boston area.

As machine learning has made its way into more and more areas of our lives, concerns about algorithmic bias have escalated. Machine learning models, which today facilitate decisions about everything from hiring and lending to medical diagnosis and criminal sentencing, may appear to be data-driven and impartial, at least to naïve users--but the typically opaque models are only as good the data they are trained on, and only as ethical as the value judgments embedded in the algorithms. The burgeoning field of algorithmic fairness, part of the much broader field of responsible computing, is aiming to remedy the situation. For several years now, along with philosophers, legal scholars, and experts in other fields, computer scientists have been tackling the issue. As Stanford University computer science professor Omer Reingold likes to put it, "We are part of the problem, and we should be part of the solution."

While I complain publically about the lack of a governing global body for A.I. regulation that's independent, most BigTech firms pretend like they regulate themselves. Nobody actually trusts that they are doing this properly. The most impressive A.I. and tech regulation I've seen is actually coming out of China. The American narrative on this is that they are anti-capitalistic. I find that attitude interesting.

In the intensifying race for global competitiveness in artificial intelligence (AI), the United States, China and the European Union are vying to be the home of what could be the most important technological revolution of our lifetimes. AI governance proposals are also developing rapidly, with the EU proposing an aggressive regulatory approach to add to its already-onerous regulatory regime. It would be imprudent for the U.S. to adopt Europe's more top-down regulatory model, however, which already decimated digital technology innovation in the past and now will do the same for AI. The key to competitive advantage in AI will be openness to entrepreneurialism, investment and talent, plus a flexible governance framework to address risks. The International Economyjournal recently asked 11 experts from Europe and the U.S. where the EU currently stood in global tech competition.

Stereotypes, bias, and discrimination have been extensively documented in Machine Learning (ML) methods such as Computer Vision (CV) [18, 80], Natural Language Processing (NLP) [6], or both, in the case of large image and caption models such as OpenAI CLIP [14]. In this paper, we evaluate how ML bias manifests in robots that physically and autonomously act within the world. We audit one of several recently published CLIP-powered robotic manipulation methods, presenting it with objects that have pictures of human faces on the surface which vary across race and gender, alongside task descriptions that contain terms associated with common stereotypes. Our experiments definitively show robots acting out toxic stereotypes with respect to gender, race, and scientifically-discredited physiognomy, at scale. Furthermore, the audited methods are less likely to recognize Women and People of Color. Our interdisciplinary sociotechnical analysis synthesizes across fields and applications such as Science Technology and Society (STS), Critical Studies, History, Safety, Robotics, and AI. We find that robots powered by large datasets and Dissolution Models (sometimes called "foundation models", e.g. CLIP) that contain humans risk physically amplifying malignant stereotypes in general; and that merely correcting disparities will be insufficient for the complexity and scale of the problem. Instead, we recommend that robot learning methods that physically manifest stereotypes or other harmful outcomes be paused, reworked, or even wound down when appropriate, until outcomes can be proven safe, effective, and just. Finally, we discuss comprehensive policy changes and the potential of new interdisciplinary research on topics like Identity Safety Assessment Frameworks and Design Justice to better understand and address these harms.